{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/github/timeseriesAI/tsai/blob/master/tutorial_nbs/02_ROCKET_a_new_SOTA_classifier.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "created by Ignacio Oguiza - email: timeseriesAI@gmail.com"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Purpose 😇"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The purpose of this notebook is to introduce you to Rocket. \n",
    "\n",
    "ROCKET (RandOm Convolutional KErnel Transform) is a new Time Series Classification (TSC) method that has just been released (Oct 29th, 2019), and has achieved **state-of-the-art performance on the UCR univariate time series classification datasets, surpassing HIVE-COTE (the previous state of the art since 2017) in accuracy, with exceptional speed compared to other traditional DL methods.** \n",
    "\n",
    "To achieve these 2 things at once is **VERY IMPRESSIVE**. ROCKET is certainly a new TSC method you should try.\n",
    "\n",
    "Authors:\n",
    "Dempster, A., Petitjean, F., & Webb, G. I. (2019). ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels. arXiv preprint arXiv:1910.13051.\n",
    "\n",
    "[paper](https://arxiv.org/pdf/1910.13051)\n",
    "\n",
    "There are 2 main limitations to the original ROCKET method though:\n",
    "- Released code doesn't handle multivariate data\n",
    "- It doesn't run on a GPU, so it's slow when used with a large datasets\n",
    "\n",
    "In this notebook you will learn: \n",
    "- how you can use the original ROCKET method\n",
    "- you will also learn about a new ROCKET version I have developed in Pytorch, that handles both **univariate and multivariate** data, and uses **GPU**\n",
    "- you will see how you can integrate the ROCKET features with fastai or other classifiers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import libraries 📚"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ## NOTE: UNCOMMENT AND RUN THIS CELL IF YOU NEED TO INSTALL/ UPGRADE TSAI\n",
    "# stable = False # True: stable version in pip, False: latest version from github\n",
    "# if stable: \n",
    "#     !pip install tsai -U >> /dev/null\n",
    "# else:      \n",
    "#     !pip install git+https://github.com/timeseriesAI/tsai.git -U >> /dev/null\n",
    "# ## NOTE: REMEMBER TO RESTART (NOT RECONNECT/ RESET) THE KERNEL/ RUNTIME ONCE THE INSTALLATION IS FINISHED"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/numba/np/ufunc/parallel.py:363: NumbaWarning: The TBB threading layer requires TBB version 2019.5 or later i.e., TBB_INTERFACE_VERSION >= 11005. Found TBB_INTERFACE_VERSION = 9107. The TBB threading layer is disabled.\n",
      "  warnings.warn(problem)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tsai       : 0.2.15\n",
      "fastai     : 2.2.5\n",
      "fastcore   : 1.3.19\n",
      "torch      : 1.7.0+cu101\n"
     ]
    }
   ],
   "source": [
    "from tsai.all import *\n",
    "computer_setup()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to use the original ROCKET method? 🚀"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ROCKET is applied in 2 phases:\n",
    "\n",
    "1. Generate features from each time series: ROCKET calculates 20k features from each time series, independently of the sequence length. \n",
    "2. Apply a classifier to those calculated features. Those features are then used by the classifier of your choice. In the original code they use 2 simple linear classifiers: RidgeClassifierCV and Logistic Regression, but you can use any classifier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1️⃣ Generate features\n",
    "\n",
    "Let's first generate the features. We'll import data from a UCR Time Series dataset.\n",
    "\n",
    "The original method requires the time series to be in a 2d array of shape (samples, len). Remember than only univariate sequences are allow in this original method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train, y_train, X_valid, y_valid = get_UCR_data('OliveOil')\n",
    "seq_len = X_train.shape[-1]\n",
    "X_train = X_train[:, 0]\n",
    "X_valid = X_valid[:, 0]\n",
    "labels = np.unique(y_train)\n",
    "transform = {}\n",
    "for i, l in enumerate(labels): transform[l] = i\n",
    "y_train = np.vectorize(transform.get)(y_train)\n",
    "y_valid = np.vectorize(transform.get)(y_valid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we normalize the data to mean 0 and std 1 **'per sample'** (recommended by the authors), that is they set each sample to mean 0 and std 1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30, 1)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = (X_train - X_train.mean(axis = 1, keepdims = True)) / (X_train.std(axis = 1, keepdims = True) + 1e-8)\n",
    "X_valid = (X_valid - X_valid.mean(axis = 1, keepdims = True)) / (X_valid.std(axis = 1, keepdims = True) + 1e-8)\n",
    "X_train.mean(axis = 1, keepdims = True).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To generate the features, we first need to create the 10k random kernels that will be used to process the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "kernels = generate_kernels(seq_len, 10000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we apply those ramdom kernels to the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_tfm = apply_kernels(X_train, kernels)\n",
    "X_valid_tfm = apply_kernels(X_valid, kernels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2️⃣ Apply a classifier\n",
    "\n",
    "So now we have the features, and we are ready to apply a classifier. \n",
    "\n",
    "Let's use a simple, linear RidgeClassifierCV as they propose in the paper. We first instantiate it. \n",
    "\n",
    "Note:\n",
    "alphas: Array of alpha values to try. Regularization strength; must be a positive float. Regularization improves the conditioning of the problem and reduces the variance of the estimates. Larger values specify stronger regularization. Alpha corresponds to C^-1 in other linear models such as LogisticRegression or LinearSVC."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import RidgeClassifierCV\n",
    "classifier = RidgeClassifierCV(alphas=np.logspace(-3, 3, 7), normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RidgeClassifierCV(alphas=array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03]),\n",
       "                  normalize=True)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " classifier.fit(X_train_tfm, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9333333333333333"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.score(X_valid_tfm, y_valid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "☣️ **This is pretty impressive! It matches or exceeds the state-of-the-art performance without any fine tuning in <2 seconds!!!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kernels = generate_kernels(seq_len, 10000)\n",
    "X_train_tfm = apply_kernels(X_train, kernels)\n",
    "X_valid_tfm = apply_kernels(X_valid, kernels)\n",
    "from sklearn.linear_model import RidgeClassifierCV\n",
    "classifier = RidgeClassifierCV(alphas=np.logspace(-3, 3, 7), normalize=True)\n",
    "classifier.fit(X_train_tfm, y_train)\n",
    "classifier.score(X_valid_tfm, y_valid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "⚠️ Bear in mind that this process is not deterministic since there is randomness involved in the kernels. In thiis case, performance may vary between .9 to .933"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to use ROCKET with large and/ or multivariate datasets on GPU? - Recommended ⭐️"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As stated before, the current ROCKET method doesn't support multivariate time series or GPU. This may be a drawback in some cases. \n",
    "\n",
    "To overcome both limitations I've created a multivariate ROCKET on GPU in Pytorch. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1️⃣ Generate features\n",
    "\n",
    "First you prepare the input data and normalize it per sample. The input to ROCKET Pytorch is a 3d tensor of shape (samples, vars, len), preferrable on gpu."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The way to use ROCKET in Pytorch is the following:\n",
    "\n",
    "* Create a dataset as you would normally do in `tsai`. \n",
    "* Create a TSDataLoaders with the following kwargs: \n",
    "    * drop_last=False. In this way we get features for every input sample.\n",
    "    * shuffle_train=False\n",
    "    * batch_tfms=[TSStandardize(by_sample=True)] so that input is normalized by sample, as recommended by the authors\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, y, splits = get_UCR_data('HandMovementDirection', split_data=False)\n",
    "tfms  = [None, [Categorize()]]\n",
    "batch_tfms = [TSStandardize(by_sample=True)]\n",
    "dls = get_ts_dls(X, y, splits=splits, tfms=tfms, drop_last=False, shuffle_train=False, batch_tfms=batch_tfms, bs=10_000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "☣️☣️ You will be able to create a dls (TSDataLoaders) object with unusually large batch sizes. I've tested it with a large dataset and a batch size = 100_000 and it worked fine. This is because ROCKET is not a usual Deep Learning model. It just applies convolutions (kernels) one at a time to create the features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instantiate a rocket model with the desired n_kernels (authors use 10_000) and kernel sizes (7, 9 and 11 in the original paper). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = build_ts_model(ROCKET, dls=dls) # n_kernels=10_000, kss=[7, 9, 11] set by default, but you can pass other values as kwargs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now generate rocket features for the entire train and valid datasets using the create_rocket_features convenience function `create_rocket_features`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we now transform the original data, creating 20k features per sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
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       "<IPython.core.display.HTML object>"
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       "<IPython.core.display.HTML object>"
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     "data": {
      "text/html": [],
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       "<IPython.core.display.HTML object>"
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     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "((160, 20000), (74, 20000))"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, y_train = create_rocket_features(dls.train, model)\n",
    "X_valid, y_valid = create_rocket_features(dls.valid, model)\n",
    "X_train.shape, X_valid.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2️⃣ Apply a classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you build the 20k features per sample, you can use them to train any classifier of your choice."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### RidgeClassifierCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now you apply a classifier of your choice. \n",
    "With RidgeClassifierCV in particular, there's no need to normalize the calculated features before passing them to the classifier, as it does it internally (if normalize is set to True as recommended by the authors)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha: 1.00E+01  train: 1.00000  valid: 0.50000\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import RidgeClassifierCV\n",
    "ridge = RidgeClassifierCV(alphas=np.logspace(-8, 8, 17), normalize=True)\n",
    "ridge.fit(X_train, y_train)\n",
    "print(f'alpha: {ridge.alpha_:.2E}  train: {ridge.score(X_train, y_train):.5f}  valid: {ridge.score(X_valid, y_valid):.5f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This result is amazing!! The previous state of the art (Inceptiontime) was .37837"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the case of other classifiers (like Logistic Regression), the authors recommend a per-feature normalization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 0 eps: 1.00E-06  C: 1.00E-05  loss: 1.35151  train_acc: 0.80000  valid_acc: 0.41892\n",
      " 1 eps: 1.00E-06  C: 1.00E-04  loss: 1.15433  train_acc: 1.00000  valid_acc: 0.45946\n",
      " 2 eps: 1.00E-06  C: 1.00E-03  loss: 0.85364  train_acc: 1.00000  valid_acc: 0.48649\n",
      " 3 eps: 1.00E-06  C: 1.00E-02  loss: 0.76183  train_acc: 1.00000  valid_acc: 0.48649\n",
      " 4 eps: 1.00E-06  C: 1.00E-01  loss: 0.74625  train_acc: 1.00000  valid_acc: 0.48649\n",
      " 5 eps: 1.00E-06  C: 1.00E+00  loss: 0.74401  train_acc: 1.00000  valid_acc: 0.48649\n",
      " 6 eps: 1.00E-06  C: 1.00E+01  loss: 0.74371  train_acc: 1.00000  valid_acc: 0.50000\n",
      " 7 eps: 1.00E-06  C: 1.00E+02  loss: 0.74367  train_acc: 1.00000  valid_acc: 0.48649\n",
      " 8 eps: 1.00E-06  C: 1.00E+03  loss: 0.74367  train_acc: 1.00000  valid_acc: 0.48649\n",
      " 9 eps: 1.00E-06  C: 1.00E+04  loss: 0.74367  train_acc: 1.00000  valid_acc: 0.48649\n",
      "10 eps: 1.00E-06  C: 1.00E+05  loss: 0.74367  train_acc: 1.00000  valid_acc: 0.48649\n",
      "\n",
      "Best result:\n",
      "eps: 1.00E-06  C: 1.00E+05  train_loss: 0.74367  train_acc: 1.00000  valid_acc: 0.48649\n"
     ]
    }
   ],
   "source": [
    "eps = 1e-6\n",
    "Cs = np.logspace(-5, 5, 11)\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "best_loss = np.inf\n",
    "for i, C in enumerate(Cs):\n",
    "    f_mean = X_train.mean(axis=0, keepdims=True)\n",
    "    f_std = X_train.std(axis=0, keepdims=True) + eps  # epsilon to avoid dividing by 0\n",
    "    X_train_tfm2 = (X_train - f_mean) / f_std\n",
    "    X_valid_tfm2 = (X_valid - f_mean) / f_std\n",
    "    classifier = LogisticRegression(penalty='l2', C=C, n_jobs=-1)\n",
    "    classifier.fit(X_train_tfm2, y_train)\n",
    "    probas = classifier.predict_proba(X_train_tfm2)\n",
    "    loss = nn.CrossEntropyLoss()(torch.tensor(probas), torch.tensor(y_train)).item()\n",
    "    train_score = classifier.score(X_train_tfm2, y_train)\n",
    "    val_score = classifier.score(X_valid_tfm2, y_valid)\n",
    "    if loss < best_loss:\n",
    "        best_eps = eps\n",
    "        best_C = C\n",
    "        best_loss = loss\n",
    "        best_train_score = train_score\n",
    "        best_val_score = val_score\n",
    "    print('{:2} eps: {:.2E}  C: {:.2E}  loss: {:.5f}  train_acc: {:.5f}  valid_acc: {:.5f}'.format(\n",
    "        i, eps, C, loss, train_score, val_score))\n",
    "print('\\nBest result:')\n",
    "print('eps: {:.2E}  C: {:.2E}  train_loss: {:.5f}  train_acc: {:.5f}  valid_acc: {:.5f}'.format(\n",
    "        best_eps, best_C, best_loss, best_train_score, best_val_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "☣️ Note: Epsilon has a large impact on the result. You can actually test several values to find the one that best fits your problem, but bear in mind you can only select C and epsilon based on train data!!! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### RandomSearch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One way to do this would be to perform a random search using several epsilon and C values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 0  eps: 1.00E-03  C: 1.00E-03  loss: 0.85501  train_acc: 1.00000  valid_acc: 0.48649\n",
      " 1  eps: 1.00E-02  C: 1.00E-03  loss: 0.86484  train_acc: 1.00000  valid_acc: 0.47297\n",
      " 2  eps: 1.00E-06  C: 1.00E+03  loss: 0.74367  train_acc: 1.00000  valid_acc: 0.48649\n",
      " 3  eps: 1.00E-04  C: 1.00E-05  loss: 1.35157  train_acc: 0.80000  valid_acc: 0.41892\n",
      " 4  eps: 1.00E-07  C: 1.00E+00  loss: 0.74401  train_acc: 1.00000  valid_acc: 0.48649\n",
      " 5  eps: 1.00E-07  C: 1.00E-03  loss: 0.85364  train_acc: 1.00000  valid_acc: 0.48649\n",
      " 6  eps: 1.00E-01  C: 1.00E-05  loss: 1.36582  train_acc: 0.93125  valid_acc: 0.40541\n",
      " 7  eps: 1.00E-06  C: 1.00E+02  loss: 0.74367  train_acc: 1.00000  valid_acc: 0.48649\n",
      " 8  eps: 1.00E-07  C: 1.00E+05  loss: 0.74367  train_acc: 1.00000  valid_acc: 0.48649\n",
      " 9  eps: 1.00E-03  C: 1.00E+02  loss: 0.74367  train_acc: 1.00000  valid_acc: 0.48649\n",
      "\n",
      "Best result:\n",
      "eps: 1.00E-07  C: 1.00E+05  train_loss: 0.74367  train_acc: 1.00000  valid_acc: 0.48649\n"
     ]
    }
   ],
   "source": [
    "n_tests = 10\n",
    "epss = np.logspace(-8, 0, 9)\n",
    "Cs = np.logspace(-5, 5, 11)\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "best_loss = np.inf\n",
    "for i in range(n_tests):\n",
    "    eps = np.random.choice(epss)\n",
    "    C = np.random.choice(Cs)\n",
    "    f_mean = X_train.mean(axis=0, keepdims=True)\n",
    "    f_std = X_train.std(axis=0, keepdims=True) + eps  # epsilon\n",
    "    X_train_tfm2 = (X_train - f_mean) / f_std\n",
    "    X_valid_tfm2 = (X_valid - f_mean) / f_std\n",
    "    classifier = LogisticRegression(penalty='l2', C=C, n_jobs=-1)\n",
    "    classifier.fit(X_train_tfm2, y_train)\n",
    "    probas = classifier.predict_proba(X_train_tfm2)\n",
    "    loss = nn.CrossEntropyLoss()(torch.tensor(probas), torch.tensor(y_train)).item()\n",
    "    train_score = classifier.score(X_train_tfm2, y_train)\n",
    "    val_score = classifier.score(X_valid_tfm2, y_valid)\n",
    "    if loss < best_loss:\n",
    "        best_eps = eps\n",
    "        best_C = C\n",
    "        best_loss = loss\n",
    "        best_train_score = train_score\n",
    "        best_val_score = val_score\n",
    "    print('{:2}  eps: {:.2E}  C: {:.2E}  loss: {:.5f}  train_acc: {:.5f}  valid_acc: {:.5f}'.format(\n",
    "        i, eps, C, loss, train_score, val_score))\n",
    "print('\\nBest result:')\n",
    "print('eps: {:.2E}  C: {:.2E}  train_loss: {:.5f}  train_acc: {:.5f}  valid_acc: {:.5f}'.format(\n",
    "        best_eps, best_C, best_loss, best_train_score, best_val_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In general, the original method may be a bit faster than the GPU method, but for larger datasets, there's a great benefit in using the GPU version."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition to this, I have also run the code on the TSC UCR multivariate datasets (all the ones that don't contain nan values), and the results are also very good, beating the previous state-of-the-art in this category as well by a large margin. For example, ROCKET reduces InceptionTime errors by 26% on average."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fastai classifier head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = concat(X_train, X_valid)\n",
    "y = concat(y_train, y_valid)\n",
    "splits = get_predefined_splits(X_train, X_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x864 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tfms  = [None, [Categorize()]]\n",
    "dsets = TSDatasets(X, y, tfms=tfms, splits=splits)\n",
    "dls   = TSDataLoaders.from_dsets(dsets.train, dsets.valid, bs=128, batch_tfms=[TSStandardize(by_var=True)])# per feature normalization\n",
    "dls.show_batch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lin_zero_init(layer):\n",
    "    if isinstance(layer, nn.Linear):\n",
    "        nn.init.constant_(layer.weight.data, 0.)\n",
    "        if layer.bias is not None: nn.init.constant_(layer.bias.data, 0.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>1.390388</td>\n",
       "      <td>0.189189</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.388322</td>\n",
       "      <td>1.380629</td>\n",
       "      <td>0.189189</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.387084</td>\n",
       "      <td>1.372374</td>\n",
       "      <td>0.405405</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.385692</td>\n",
       "      <td>1.367556</td>\n",
       "      <td>0.391892</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.385801</td>\n",
       "      <td>1.398611</td>\n",
       "      <td>0.202703</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.384942</td>\n",
       "      <td>1.473902</td>\n",
       "      <td>0.202703</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.385203</td>\n",
       "      <td>1.420622</td>\n",
       "      <td>0.216216</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1.383171</td>\n",
       "      <td>1.356875</td>\n",
       "      <td>0.405405</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.381003</td>\n",
       "      <td>1.359321</td>\n",
       "      <td>0.405405</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1.377475</td>\n",
       "      <td>1.399668</td>\n",
       "      <td>0.189189</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>1.373955</td>\n",
       "      <td>1.389980</td>\n",
       "      <td>0.202703</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>1.369959</td>\n",
       "      <td>1.347681</td>\n",
       "      <td>0.405405</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>1.365103</td>\n",
       "      <td>1.443242</td>\n",
       "      <td>0.189189</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>1.363665</td>\n",
       "      <td>1.380452</td>\n",
       "      <td>0.229730</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>1.359832</td>\n",
       "      <td>1.330192</td>\n",
       "      <td>0.405405</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>1.356887</td>\n",
       "      <td>1.408442</td>\n",
       "      <td>0.243243</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>1.353494</td>\n",
       "      <td>1.478218</td>\n",
       "      <td>0.189189</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1.349370</td>\n",
       "      <td>1.374748</td>\n",
       "      <td>0.256757</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>1.343981</td>\n",
       "      <td>1.312094</td>\n",
       "      <td>0.405405</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>1.338318</td>\n",
       "      <td>1.337045</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1.333844</td>\n",
       "      <td>1.358525</td>\n",
       "      <td>0.337838</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>1.327750</td>\n",
       "      <td>1.429753</td>\n",
       "      <td>0.202703</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>1.322031</td>\n",
       "      <td>1.458103</td>\n",
       "      <td>0.189189</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>1.318513</td>\n",
       "      <td>1.369135</td>\n",
       "      <td>0.283784</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>1.311853</td>\n",
       "      <td>1.343879</td>\n",
       "      <td>0.324324</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>1.306150</td>\n",
       "      <td>1.354887</td>\n",
       "      <td>0.270270</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>1.301080</td>\n",
       "      <td>1.337248</td>\n",
       "      <td>0.432432</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>1.295619</td>\n",
       "      <td>1.340356</td>\n",
       "      <td>0.351351</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>1.289412</td>\n",
       "      <td>1.363868</td>\n",
       "      <td>0.256757</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>1.283790</td>\n",
       "      <td>1.389427</td>\n",
       "      <td>0.229730</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>1.278791</td>\n",
       "      <td>1.362788</td>\n",
       "      <td>0.270270</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>1.274324</td>\n",
       "      <td>1.324322</td>\n",
       "      <td>0.418919</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>1.268557</td>\n",
       "      <td>1.313734</td>\n",
       "      <td>0.513514</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>1.263377</td>\n",
       "      <td>1.321581</td>\n",
       "      <td>0.405405</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>1.259315</td>\n",
       "      <td>1.331173</td>\n",
       "      <td>0.310811</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>1.254296</td>\n",
       "      <td>1.339095</td>\n",
       "      <td>0.283784</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>1.250026</td>\n",
       "      <td>1.333421</td>\n",
       "      <td>0.337838</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>1.244938</td>\n",
       "      <td>1.328161</td>\n",
       "      <td>0.418919</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>1.240129</td>\n",
       "      <td>1.329505</td>\n",
       "      <td>0.391892</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>1.235639</td>\n",
       "      <td>1.333527</td>\n",
       "      <td>0.378378</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>1.231234</td>\n",
       "      <td>1.338217</td>\n",
       "      <td>0.364865</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>1.227223</td>\n",
       "      <td>1.342596</td>\n",
       "      <td>0.364865</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>1.223684</td>\n",
       "      <td>1.342737</td>\n",
       "      <td>0.364865</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>1.220215</td>\n",
       "      <td>1.340966</td>\n",
       "      <td>0.351351</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>1.216687</td>\n",
       "      <td>1.338499</td>\n",
       "      <td>0.364865</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>1.213320</td>\n",
       "      <td>1.336110</td>\n",
       "      <td>0.351351</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>1.209550</td>\n",
       "      <td>1.334511</td>\n",
       "      <td>0.378378</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>1.206020</td>\n",
       "      <td>1.333629</td>\n",
       "      <td>0.378378</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>1.202567</td>\n",
       "      <td>1.333249</td>\n",
       "      <td>0.391892</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>1.199693</td>\n",
       "      <td>1.333144</td>\n",
       "      <td>0.391892</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = create_mlp_head(dls.vars, dls.c, dls.len)\n",
    "model.apply(lin_zero_init)\n",
    "learn = Learner(dls, model, metrics=accuracy, cbs=ShowGraph())\n",
    "learn.fit_one_cycle(50, lr_max=1e-4)\n",
    "learn.plot_metrics()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3783783783783784"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eps = 1e-6\n",
    "\n",
    "# normalize 'per feature'\n",
    "f_mean = X_train.mean(axis=0, keepdims=True)\n",
    "f_std = X_train.std(axis=0, keepdims=True) + eps\n",
    "X_train_norm = (X_train - f_mean) / f_std\n",
    "X_valid_norm = (X_valid - f_mean) / f_std\n",
    "\n",
    "import xgboost as xgb\n",
    "classifier = xgb.XGBClassifier(max_depth=3,\n",
    "                               learning_rate=0.1,\n",
    "                               n_estimators=100,\n",
    "                               verbosity=1,\n",
    "                               objective='binary:logistic',\n",
    "                               booster='gbtree',\n",
    "                               tree_method='auto',\n",
    "                               n_jobs=-1,\n",
    "                               gpu_id=default_device().index,\n",
    "                               gamma=0,\n",
    "                               min_child_weight=1,\n",
    "                               max_delta_step=0,\n",
    "                               subsample=.5,\n",
    "                               colsample_bytree=1,\n",
    "                               colsample_bylevel=1,\n",
    "                               colsample_bynode=1,\n",
    "                               reg_alpha=0,\n",
    "                               reg_lambda=1,\n",
    "                               scale_pos_weight=1,\n",
    "                               base_score=0.5,\n",
    "                               random_state=0,\n",
    "                               missing=None)\n",
    "\n",
    "classifier.fit(X_train_norm, y_train)\n",
    "preds = classifier.predict(X_valid_norm)\n",
    "(preds == y_valid).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ROCKET is a great method for TSC that has established a new level of performance both in terms of accuracy and time. It does it by successfully applying an approach quite different from the traditional DL approaches. The method uses 10k random kernels to generate features that are then classified by linear classifiers (although you may use a classifier of your choice).\n",
    "The original method has 2 limitations (lack of multivariate and lack of GPU support) that are overcome by the Pytorch implementation shared in this notebook.\n",
    "\n",
    "So this is all the code you need to train a state-of-the-art model using rocket and GPU in `tsai`:\n",
    "\n",
    "```\n",
    "X, y, splits = get_UCR_data('HandMovementDirection', return_split=False)\n",
    "tfms  = [None, [Categorize()]]\n",
    "batch_tfms = [TSStandardize(by_sample=True)]\n",
    "dsets = TSDatasets(X, y, tfms=tfms, splits=splits)\n",
    "dls = TSDataLoaders.from_dsets(dsets.train, dsets.valid, bs=64, drop_last=False, shuffle_train=False, batch_tfms=[TSStandardize(by_sample=True)])\n",
    "model = create_model(ROCKET, dls=dls)\n",
    "X_train, y_train = create_rocket_features(dls.train, model)\n",
    "X_valid, y_valid = create_rocket_features(dls.valid, model)\n",
    "ridge = RidgeClassifierCV(alphas=np.logspace(-8, 8, 17), normalize=True)\n",
    "ridge.fit(X_train, y_train)\n",
    "print(f'alpha: {ridge.alpha_:.2E}  train: {ridge.score(X_train, y_train):.5f}  valid: {ridge.score(X_valid, y_valid):.5f}')\n",
    "```"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
